A new method for predicting technological trends based on the analysis of scientific articles and patents

Thanh Viet Nguyen, A.G. Kravets


To achieve competitiveness in a rapidly changing science, it is important to follow the development of existing technologies and discover new and promising technologies. Firms need to develop a technology development strategy by predicting technology trends in order to gain a competitive advantage while using limited resources. On the other hand, nowadays the number of scientific articles, patents and other miscellaneous data is growing at a rapid pace, and it becomes impossible to stay up to date with everything that is published. However, despite all efforts, none of existing methodological and technological results are able to create models and methods for the holistic perception of heterogeneous information by a computing system – scientific publications and patents, which is contained in open sources. At the same time, most of the existing studies are intended for the analysis and early detection of new technologies or monitoring trends in some specific technology industries, without considering the solution of the problem of predicting many different technology trends. In addition, the accuracy of the assessment of the proposed methods in existing studies is either rather low (the maximum metric F1 for assessing the accuracy of the forecast is ~ 74%), or is absent (the quality of the method has not been assessed). Thus, this article proposes a new method for analyzing and predicting technological trends based on the processing of heterogeneous data (scientific articles, patents) from open sources by developing an algorithm for extracting significant keywords and methods for creating co-occurrence matrices of elements (keywords, CPC codes).

Full Text:

PDF (Russian)


Klimenko A.G., Zajcev K.S. Issledovanie podhodov k razrabotke umnyh ob#ektov // International journal of open information technologies. 2022. Vol. 10, № 6. pp. 141–148.

Marr B. The 4 Biggest Trends In Big Data And Analytics Right For 2021 [Jelektronnyj resurs] // Forbes. 2021. URL: forbes.com/sites/bernardmarr/2021/02/22/the-4-biggest-trends-in-big-

data-and-analytics-right-for-2021/ (data obrashhenija: 12.07.2022).

Kravec A.G., Sal'nikova N.A. Predskazatel'noe modelirovanie trendov tehnologicheskogo razvitija // Izvestija Sankt-Peterburgskogo gosudarstvennogo tehnologicheskogo instituta (tehnicheskogo universiteta). 2020. № 55 (81). pp. 103–108.

Viet N.T., Kravets A., Duong Q.H.T. Data Mining Methods for Analysis and Forecast of an Emerging Technology Trend: A Systematic Mapping Study from SCOPUS Papers // Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2021. pp. 81–101.

Viet N.T., Gneushev V. Analyzing and Forecasting Emerging Technology Trends by Mining Web News // Communications in Computer and Information Science. 2021. pp. 55–69.

Lee C. A review of data analytics in technological forecasting // Technological Forecasting and Social Change. 2021.

Nguen T.V., Kravec A.G., Shherbakov M.V. Metod dlja analiza tendencij razvitija tehnologij upravlenija jeffektivnost'ju aktivov // Prikaspijskij zhurnal: upravlenie i vysokie tehnologii. 2022. Vol. 57, № 1. pp. 39–53.

Viet N.T., Kravets A.G. Analyzing Recent Research Trends of Computer Science from Academic Open-access Digital Library // 2019 8th International Conference System Modeling and Advancement in Research Trends (SMART). IEEE, 2019. pp. 31–36.

Kravets A.G., Vasiliev S.S., Shabanov D. V. Research of the LDA Algorithm Results for Patents Texts Processing // 2018 9th International Conference on Information, Intelligence, Systems and Applications (IISA). IEEE, 2018. pp. 1–6.

Zhou Y. i dr. Forecasting emerging technologies using data augmentation and deep learning // Scientometrics. 2020.

Song K., Kim K., Lee S. Identifying promising technologies using patents: A retrospective feature analysis and a prospective needs analysis on outlier patents // Technological Forecasting and Social Change. 2018. Vol. 128. pp. 118–132.

Rotolo D., Hicks D., Martin B.R. What is an emerging technology? // Research Policy. 2015. Vol. 44, № 10. pp. 1827–1843.

Ena O. i dr. A methodology for technology trend monitoring: the case of semantic technologies // Scientometrics. 2016. Vol. 108, № 3. pp. 1013–1041.

Li X. i dr. Forecasting technology trends using text mining of the gaps between science and technology: The case of perovskite solar cell technology // Technological Forecasting and Social Change. 2019. Vol. 146. pp. 432–449.

Wang M.-Y., Fang S.-C., Chang Y.-H. Exploring technological opportunities by mining the gaps between science and technology: Microalgal biofuels // Technological Forecasting and Social Change. 2015. Vol. 92. pp. 182–195.

Wei F. i dr. Decreasing the noise of scientific citations in patents to measure knowledge flow // 17th International Conference on Scientometrics and Informetrics, ISSI 2019 - Proceedings. 2019. pp. 1662–1669.

Suominen A., Ranaei S., Dedehayir O. Exploration of Science and Technology Interaction: A Case Study on Taxol // IEEE Transactions on Engineering Management. 2021. Vol. 68, № 6. pp. 1786–1801.

Li X. i dr. Monitoring and forecasting the development trends of nanogenerator technology using citation analysis and text mining // Nano Energy. 2020. Vol. 71. pp. 104636.

Nguen T. V., Kravec A. G. Ocenka i prognozirovanie tendencij razvitija nauchnyh issledovanij na osnove bibliometricheskogo analiza publikacij // Informacionnye tehnologii. 2021. Vol. 27, № 4. pp. 195–201.

Industrial-Strength Natural Language Processing [Jelektronnyj resurs]. 2022. URL: https://spacy.io/ (data obrashhenija: 26.02.2022).

Natural Language Toolkit [Jelektronnyj resurs]. 2022. URL: https://www.nltk.org/index.html (data obrashhenija: 27.02.2022).

SentenceTransformers Documentation [Jelektronnyj resurs]. 2022. URL: https://www.sbert.net/ (data obrashhenija: 26.02.2022).

KeyBERT - Quickstart [Jelektronnyj resurs]. 2022. URL: https://maartengr.github.io/KeyBERT/guides/quickstart.html (data obrashhenija: 26.02.2022).

Schulz E., Speekenbrink M., Krause A. A tutorial on Gaussian process regression: Modelling, exploring, and exploiting functions // Journal of Mathematical Psychology. 2018.

About The Lens [Jelektronnyj resurs]. 2022. URL: https://about.lens.org/ (data obrashhenija: 19.06.2022).

van Eck N.J., Waltman L. Visualizing Bibliometric Networks // Measuring Scholarly Impact. Cham: Springer International Publishing, 2014. S. 285–320.

Chiba C. Life After USPC. 5 Things Patent Searchers Should Know About CPC [Jelektronnyj resurs]. URL: lexisnexis.de/expertenbeitraege-webinare/downloads/whitepaper/life-after-uspc (data obrashhenija: 12.07.2022).

Marr B. Tech Trends in Practice : The 25 technologies that are driving the 4th Industrial Revolution // Machine Learning Process. 2020.

Bernard Marr. These 25 Technology Trends Will Define The Next Decade // Forbes. 2020.

Bernard Marr [Jelektronnyj resurs]. URL: https://www.forbes.com/sites/bernardmarr (data obrashhenija: 12.07.2022).

Marr B. The 5 Big Problems With Blockchain Everyone Should Be Aware Of // Forbes. 2018.


  • There are currently no refbacks.

Abava  Absolutech Convergent 2022

ISSN: 2307-8162